Event Sequences (EvS ) refer to sequential data characterized by irregular sampling intervals and a mix of categorical and numerical features. Accurate classification of these sequences is crucial for various real-life applications, including healthcare, finance, and user interaction. Despite the popularity of the EvS classification task, there is currently no standardized benchmark or rigorous evaluation protocol. This lack of standardization makes it difficult to compare results across studies, which can result in unreliable conclusions and hinder progress in the field. To address this gap, we present EBES, a comprehensive benchmark for EvS classification with sequence-level targets. EBES features standardized evaluation scenarios and protocols, along with an open-source PyTorch library. Code is available at https://github.com/On-Point-RND/EBES. Preprocessed data is available at https://huggingface.co/datasets/On-Point-Rnd/ebes that implements 9 modern models. Additionally, it includes the largest collection of EvS datasets, featuring 10 curated datasets, including a novel synthetic dataset and real-world data with the largest publicly available banking dataset. The library offers user-friendly interfaces for integrating new methods and datasets. Our benchmarking results highlight the unique properties of EvS compared to other sequential data types, provide a performance ranking of modern models-with GRU-based models achieving the best results-and reveal the challenges associated with robust EvS learning. The goal of EBES is to facilitate reproducible research, expedite progress in the field, and increase the real-world impact of EvS classification techniques.

As a company, proper financial planning is challenging. The knowledge is specific and competent experts are scarce. Poor financial management has a high cost. It results in penalty fees, missed opportunities, and return on investment. CAUSALYSIS empowers small and medium businesses with financial scenario planning powered by Causal Machine Learning. We describe a use case for causal machine learning on the ROI of property rentals.

Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in the NLP community, posing a challenge due to their tremendous number of parameters and limited robustness. In particular, deep-learning models are vulnerable to adversarial attacks: a little change in the input harms the model's output. In this work, we examine adversarial attacks on transaction records data and defenses from these attacks. The transaction records data have a different structure than the canonical NLP or time-series data, as neighboring records are less connected than words in sentences, and each record consists of both discrete merchant code and continuous transaction amount. We consider a black-box attack scenario, where the attack doesn't know the true decision model and pay special attention to adding transaction tokens to the end of a sequence. These limitations provide a more realistic scenario, previously unexplored in the NLP world. The proposed adversarial attacks and the respective defenses demonstrate remarkable performance using relevant datasets from the financial industry. Our results show that a couple of generated transactions are sufficient to fool a deep-learning model. Further, we improve model robustness via adversarial training or separate adversarial examples detection. This work shows that embedding protection from adversarial attacks improves model robustness, allowing a wider adoption of deep models for transaction records in banking and finance.

Recommender systems have become an essential instrument in a wide range of industries to personalize the user experience. A significant issue that has captured both researchers' and industry experts' attention is the cold start problem for new items. This work presents a graph neural network recommender system using item hierarchy graphs and a bespoke architecture to handle the cold start case for items. The experimental study on multiple datasets and millions of users and interactions indicates that our method achieves better forecasting quality than the state-of-the-art with a comparable computational time.

Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In this work, a system developed for a leading provider of cloud computing combining both user segmentation and demand forecasting is presented. It consists of a TDA-based clustering method for time series inspired by a popular managerial framework for customer segmentation and extended to the case of clusterwise regression using matrix factorization methods to forecast demand. Increasing customer loyalty and producing accurate forecasts remain active topics of discussion both for researchers and managers. Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level with significantly higher accuracy than a state of the art baseline. This work thus seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.

We consider an unknown smooth function which maps high-dimensional inputs to multidimensional outputs and whose domain of definition is an unknown low-dimensional input manifold embedded in an ambient high-dimensional input space. Given a training dataset with "input-output" pairs, Regression with Manifold Valued Inputs problem is to estimate the unknown function and its Jacobian matrix. Previously proposed solutions are very computationally expensive. The paper presents a new geometrically motivated kernel regression method for solving the considered problem with a much lower computational complexity while preserving accuracy.

In the present work, we study the candidate biomarkers for the depression disorder and the depression + epilepsy comorbidity. Building on the advanced data analysis pipeline, we identify candidate biomarkers, compare them across tasks and to the previous research. The classification performance achieved by our system compares favourably to the one reported in literature, where longer scanning sessions and stronger magnetic fields were employed.